OpenRaiser/Intern-Atlas huggingface dataset:
Assembly in populations of social networks
Venue: arXiv cs.SI 2018
Year: 2018

In-depth studies of sociotechnical systems are largely limited to single instances. Network surveys are expensive, and platforms vary in important ways, from interface design, to social norms, to historical contingencies. With single examples, we can not in general know how much of observed network structure is explained by historical accidents, random noise, or meaningful social processes, nor can we claim that network structure predicts outcomes, such as organization success or ecosystem health. Here, I show how we can adopt a comparative approach for settings where we have, or can cleverly construct, multiple instances of a network to estimate the natural variability in social systems. The comparative approach makes previously untested theories testable. Drawing on examples from the social networks literature, I discuss emerging directions in the study of populations of sociotechnical systems using insights from organization theory and ecology.
Bias Disparity in Recommendation Systems
Venue: arXiv cs.IR 2018
Year: 2018

Recommender systems have been applied successfully in a number of different domains, such as, entertainment, commerce, and employment. Their success lies in their ability to exploit the collective behavior of users in order to deliver highly targeted, personalized recommendations. Given that recommenders learn from user preferences, they incorporate different biases that users exhibit in the input data. More importantly, there are cases where recommenders may amplify such biases, leading to the phenomenon of bias disparity. In this short paper, we present a preliminary experimental study on synthetic data, where we investigate different conditions under which a recommender exhibits bias disparity, and the long-term effect of recommendations on data bias. We also consider a simple re-ranking algorithm for reducing bias disparity, and present some observations for data disparity on real data.
The one comparing narrative social network extraction techniques
Venue: arXiv cs.SI 2018
Year: 2018

Analysing narratives through their social networks is an expanding field in quantitative literary studies. Manually extracting a social network from any narrative can be time consuming, so automatic extraction methods of varying complexity have been developed. However, the effect of different extraction methods on the analysis is unknown. Here we model and compare three extraction methods for social networks in narratives: manual extraction, co-occurrence automated extraction and automated extraction using machine learning. Although the manual extraction method produces more precise results in the network analysis, it is much more time consuming and the automatic extraction methods yield comparable conclusions for density, centrality measures and edge weights. Our results provide evidence that social networks extracted automatically are reliable for many analyses. We also describe which aspects of analysis are not reliable with such a social network. We anticipate that our findings will make it easier to analyse more narratives, which help us improve our understanding of how stories are written and evolve, and how people interact with each other.
Medical code prediction with multi-view convolution and description-regularized label-dependent attention
Venue: arXiv cs.CL 2018
Year: 2018

A ubiquitous task in processing electronic medical data is the assignment of standardized codes representing diagnoses and/or procedures to free-text documents such as medical reports. This is a difficult natural language processing task that requires parsing long, heterogeneous documents and selecting a set of appropriate codes from tens of thousands of possibilities---many of which have very few positive training samples. We present a deep learning system that advances the state of the art for the MIMIC-III dataset, achieving a new best micro F1-measure of 55.85\%, significantly outperforming the previous best result (Mullenbach et al. 2018). We achieve this through a number of enhancements, including two major novel contributions: multi-view convolutional channels, which effectively learn to adjust kernel sizes throughout the input; and attention regularization, mediated by natural-language code descriptions, which helps overcome sparsity for thousands of uncommon codes. These and other modifications are selected to address difficulties inherent to both automated coding specifically and deep learning generally. Finally, we investigate our accuracy results in detail to individually measure the impact of these contributions and point the way towards future algorithmic improvements.
DSIC: Deep Learning based Self-Interference Cancellation for In-Band Full Duplex Wireless
Venue: arXiv cs.NI 2018
Year: 2018

In-band full duplex wireless is of utmost interest to future wireless communication and networking due to great potentials of spectrum efficiency. IBFD wireless, however, is throttled by its key challenge, namely self-interference. Therefore, effective self-interference cancellation is the key to enable IBFD wireless. This paper proposes a real-time non-linear self-interference cancellation solution based on deep learning. In this solution, a self-interference channel is modeled by a deep neural network (DNN). Synchronized self-interference channel data is first collected to train the DNN of the self-interference channel. Afterwards, the trained DNN is used to cancel the self-interference at a wireless node. This solution has been implemented on a USRP SDR testbed and evaluated in real world in multiple scenarios with various modulations in transmitting information including numbers, texts as well as images. It results in the performance of 17dB in digital cancellation, which is very close to the self-interference power and nearly cancels the self-interference at a SDR node in the testbed. The solution yields an average of 8.5% bit error rate (BER) over many scenarios and different modulation schemes.
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Despite the success of deep learning for static image understanding, it remains unclear what are the most effective network architectures for the spatial-temporal modeling in videos. In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatial temporal network (StNet) architecture for both local and global spatial-temporal modeling in videos. Particularly, StNet stacks N successive video frames into a \emph{super-image} which has 3N channels and applies 2D convolution on super-images to capture local spatial-temporal relationship. To model global spatial-temporal relationship, we apply temporal convolution on the local spatial-temporal feature maps. Specifically, a novel temporal Xception block is proposed in StNet. It employs a separate channel-wise and temporal-wise convolution over the feature sequence of video. Extensive experiments on the Kinetics dataset demonstrate that our framework outperforms several state-of-the-art approaches in action recognition and can strike a satisfying trade-off between recognition accuracy and model complexity. We further demonstrate the generalization performance of the leaned video representations on the UCF101 dataset.
Almost Optimal Distance Oracles for Planar Graphs
Venue: arXiv cs.DS 2018
Year: 2018

We present new tradeoffs between space and query-time for exact distance oracles in directed weighted planar graphs. These tradeoffs are almost optimal in the sense that they are within polylogarithmic, sub-polynomial or arbitrarily small polynomial factors from the na\"ıve linear space, constant query-time lower bound. These tradeoffs include: (i) an oracle with space $\tilde{O}(n^{1+ε})$ and query-time $\tilde{O}(1)$ for any constant $ε>0$, (ii) an oracle with space $\tilde{O}(n)$ and query-time $\tilde{O}(n^ε)$ for any constant $ε>0$, and (iii) an oracle with space $n^{1+o(1)}$ and query-time $n^{o(1)}$.
You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization
Venue: arXiv cs.CV 2018
Year: 2018

Recently Neural Architecture Search (NAS) has aroused great interest in both academia and industry, however it remains challenging because of its huge and non-continuous search space. Instead of applying evolutionary algorithm or reinforcement learning as previous works, this paper proposes a Direct Sparse Optimization NAS (DSO-NAS) method. In DSO-NAS, we provide a novel model pruning view to NAS problem. In specific, we start from a completely connected block, and then introduce scaling factors to scale the information flow between operations. Next, we impose sparse regularizations to prune useless connections in the architecture. Lastly, we derive an efficient and theoretically sound optimization method to solve it. Our method enjoys both advantages of differentiability and efficiency, therefore can be directly applied to large datasets like ImageNet. Particularly, On CIFAR-10 dataset, DSO-NAS achieves an average test error 2.84\%, while on the ImageNet dataset DSO-NAS achieves 25.4\% test error under 600M FLOPs with 8 GPUs in 18 hours.
Polyadic cyclic codes over a non-chain ring $\mathbb{F}_{q}[u,v]/\langle f(u),g(v), uv-vu\rangle$
Venue: arXiv cs.IT 2018
Year: 2018

Let $f(u)$ and $g(v)$ be any two polynomials of degree $k$ and $\ell$ respectively ($k$ and $\ell$ are not both $1$), which split into distinct linear factors over $\mathbb{F}_{q}$. Let $\mathcal{R}=\mathbb{F}_{q}[u,v]/\langle f(u),g(v),uv-vu\rangle$ be a finite commutative non-chain ring. In this paper, we study polyadic codes and their extensions over the ring $\mathcal{R}$. We give examples of some polyadic codes which are optimal with respect to Griesmer type bound for rings. A Gray map is defined from $\mathcal{R}^n \rightarrow \mathbb{F}^{k\ell n}_q$ which preserves duality. The Gray images of polyadic codes and their extensions over the ring $\mathcal{R}$ lead to construction of self-dual, isodual, self-orthogonal and complementary dual (LCD) codes over $\mathbb{F}_q$. Some examples are also given to illustrate this.
Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques
Venue: arXiv cs.CV 2018
Year: 2018

Road crashes and related forms of accidents are a common cause of injury and death among the human population. According to 2015 data from the World Health Organization, road traffic injuries resulted in approximately 1.25 million deaths worldwide, i.e. approximately every 25 seconds an individual will experience a fatal crash. While the cost of traffic accidents in Europe is estimated at around 160 billion Euros, driver drowsiness accounts for approximately 100,000 accidents per year in the United States alone as reported by The American National Highway Traffic Safety Administration (NHTSA). In this paper, a novel approach towards real-time drowsiness detection is proposed. This approach is based on a deep learning method that can be implemented on Android applications with high accuracy. The main contribution of this work is the compression of heavy baseline model to a lightweight model. Moreover, minimal network structure is designed based on facial landmark key point detection to recognize whether the driver is drowsy. The proposed model is able to achieve an accuracy of more than 80%. Keywords: Driver Monitoring System; Drowsiness Detection; Deep Learning; Real-time Deep Neural Network; Android.
Revisiting size effects in higher education research productivity
Venue: arXiv cs.DL 2018
Year: 2018

The potential occurrence of variable returns to size in research activity is a factor to be considered in choices about the size of research organizations and also in the planning of national research assessment exercises, so as to avoid favoring those organizations that would benefit from such occurrence. The aim of the current work is to improve on weaknesses in past inquiries concerning returns to size through application of a research productivity measurement methodology that is more accurate and robust. The method involves field-standardized measurements that are free of the typical distortions of aggregate measurement by discipline or organization. The analysis is conducted for 183 hard science fields in all 77 Italian universities (time period 2004-2008) and allows detection of potential differences by field.
Supply Chain Management analysis: a simulation approach of the Value Chain Operations Reference model (VCOR)
Venue: arXiv cs.MA 2018
Year: 2018

The impact of globalization and worldwide competition has forced firms to modify their strategies towards a real time operation with respect to customer's requirements. This behaviour, together with the communication possibilities offered by the actual Information and Communication Technologies, allows the top management to move towards the concept of extended enterprise in which a collaborative link is established among suppliers, commercial partners and customers. When the information flows involve each actor of the chain, from suppliers to the final distribution centers, the extended enterprise becomes a virtual firm, that can be defined as a set of stand-alone operational units that acts to reconfigure themselves as a value chain in order to adapt to the business opportunities given by the market. The present work is intended to verify through a simulation approach the quantitative advantages that can be obtained by the introduction of the Value Chain concept into the Supply Chain Management (SCM). The paper, after a description of the two most known (SCM) methods - SCOR and VCOR - makes a comparison between them by the customer's point of view. In the second part of the work a simulation model has been developed to verify the advantage that the VCOR is able to obtain, validating it on an industrial case study.
GEMRank: Global Entity Embedding For Collaborative Filtering
Venue: arXiv cs.IR 2018
Year: 2018

Recently, word embedding algorithms have been applied to map the entities of recommender systems, such as users and items, to new feature spaces using textual element-context relations among them. Unlike many other domains, this approach has not achieved a desired performance in collaborative filtering problems, probably due to unavailability of appropriate textual data. In this paper we propose a new recommendation framework, called GEMRank that can be applied when the user-item matrix is the sole available souce of information. It uses the concept of profile co-occurrence for defining relations among entities and applies a factorization method for embedding the users and items. GEMRank then feeds the extracted representations to a neural network model to predict user-item like/dislike relations which the final recommendations are made based on. We evaluated GEMRank in an extensive set of experiments against state of the art recommendation methods. The results show that GEMRank significantly outperforms the baseline algorithms in a variety of data sets with different degrees of density.
Assessing the varying level of impact measurement accuracy as a function of the citation window length
Venue: arXiv cs.DL 2018
Year: 2018

With the passage of more time from the original date of publication, the measure of the impact of scientific works using subsequent citation counts becomes more accurate. However the measurement of individual and organizational research productivity should ideally refer to a period with closing date just prior to the evaluation exercise. Therefore it is necessary to compromise between accuracy and timeliness. This work attempts to provide an order of magnitude for the error in measurement that occurs with decreasing the time lapse between date of publication and citation count. The analysis is conducted by scientific discipline on the basis of publications indexed in the Thomson Reuters Italian National Citation Report.
Weakly Supervised Grammatical Error Correction using Iterative Decoding
Venue: arXiv cs.CL 2018
Year: 2018

We describe an approach to Grammatical Error Correction (GEC) that is effective at making use of models trained on large amounts of weakly supervised bitext. We train the Transformer sequence-to-sequence model on 4B tokens of Wikipedia revisions and employ an iterative decoding strategy that is tailored to the loosely-supervised nature of the Wikipedia training corpus. Finetuning on the Lang-8 corpus and ensembling yields an F0.5 of 58.3 on the CoNLL'14 benchmark and a GLEU of 62.4 on JFLEG. The combination of weakly supervised training and iterative decoding obtains an F0.5 of 48.2 on CoNLL'14 even without using any labeled GEC data.
Issues in the software implementation of stochastic numerical Runge-Kutta
Venue: arXiv cs.NA 2018
Year: 2018

This paper discusses stochastic numerical methods of Runge-Kutta type with weak and strong convergences for systems of stochastic differential equations in Itô form. At the beginning we give a brief overview of the stochastic numerical methods and information from the theory of stochastic differential equations. Then we motivate the approach to the implementation of these methods using source code generation. We discuss the implementation details and the used programming languages and libraries
Learning Shared Dynamics with Meta-World Models
Venue: arXiv cs.AI 2018
Year: 2018

Humans have consciousness as the ability to perceive events and objects: a mental model of the world developed from the most impoverished of visual stimuli, enabling humans to make rapid decisions and take actions. Although spatial and temporal aspects of different scenes are generally diverse, the underlying physics among environments still work the same way, thus learning an abstract description of shared physical dynamics helps human to understand the world. In this paper, we explore building this mental world with neural network models through multi-task learning, namely the meta-world model. We show through extensive experiments that our proposed meta-world models successfully capture the common dynamics over the compact representations of visually different environments from Atari Games. We also demonstrate that agents equipped with our meta-world model possess the ability of visual self-recognition, i.e., recognize themselves from the reflected mirrored environment derived from the classic mirror self-recognition test (MSR).
The dangers of performance-based research funding in non-competitive higher education systems
Venue: arXiv cs.DL 2018
Year: 2018

An increasing number of nations allocate public funds to research institutions on the basis of rankings obtained from national evaluation exercises. Therefore, in non-competitive higher education systems where top scientists are dispersed among all the universities, rather than concentrated among a few, there is a high risk of penalizing those top scientists who work in lower-performance universities. Using a five-year bibliometric analysis conducted on all Italian universities active in the hard sciences from 2004-2008, this work analyzes the distribution of publications and relevant citations by scientists within the universities, measures the research performance of individual scientists, quantifies the intensity of concentration of top scientists at each university, provides performance rankings for the universities, and indicates the effects of selective funding on the top scientists of low-ranked universities.
The relationship between scientists' research performance and the degree of internationalization of their research
Venue: arXiv cs.DL 2018
Year: 2018

Policy makers, at various levels of governance, generally encourage the development of research collaboration. However the underlying determinants of collaboration are not completely clear. In particular, the literature lacks studies that, taking the individual researcher as the unit of analysis, attempt to understand if and to what extent the researcher's scientific performance might impact on his/her degree of collaboration with foreign colleagues. The current work examines the international collaborations of Italian university researchers for the period 2001-2005, and puts them in relation to each individual's research performance. The results of the investigation, which assumes co-authorship as proxy of research collaboration, show that both research productivity and average quality of output have positive effects on the degree of international collaboration achieved by a scientist.
Are researchers that collaborate more at the international level top performers? An investigation on the Italian university system
Venue: arXiv cs.DL 2018
Year: 2018

The practice of collaboration, and particularly international collaboration, is becoming ever more widespread in scientific research, and is likewise receiving greater interest and stimulus from policy-makers. However, the relation between research performance and degree of internationalization at the level of single researchers still presents unresolved questions. The present work, through a bibliometric analysis of the entire Italian university population working in the hard sciences over the period 2001-2005, seeks to answer some of these questions. The results show that the researchers with top performance with respect to their national colleagues are also those who collaborate more abroad, but that the reverse is not always true. Also, interesting differences emerge at the sectorial level. Finally, the effect of the nation involved in the international partnership plays a role that should not be ignored.
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls
Venue: arXiv cs.LG 2018
Year: 2018

Machine learning has been applied to a broad range of applications and some of them are available online as application programming interfaces (APIs) with either free (trial) or paid subscriptions. In this paper, we study adversarial machine learning in the form of back-box attacks on online classifier APIs. We start with a deep learning based exploratory (inference) attack, which aims to build a classifier that can provide similar classification results (labels) as the target classifier. To minimize the difference between the labels returned by the inferred classifier and the target classifier, we show that the deep learning based exploratory attack requires a large number of labeled training data samples. These labels can be collected by calling the online API, but usually there is some strict rate limitation on the number of allowed API calls. To mitigate the impact of limited training data, we develop an active learning approach that first builds a classifier based on a small number of API calls and uses this classifier to select samples to further collect their labels. Then, a new classifier is built using more training data samples. This updating process can be repeated multiple times. We show that this active learning approach can build an adversarial classifier with a small statistical difference from the target classifier using only a limited number of training data samples. We further consider evasion and causative (poisoning) attacks based on the inferred classifier that is built by the exploratory attack. Evasion attack determines samples that the target classifier is likely to misclassify, whereas causative attack provides erroneous training data samples to reduce the reliability of the re-trained classifier. The success of these attacks show that adversarial machine learning emerges as a feasible threat in the realistic case with limited training data.